Research And Application Of Cross-Media Retrieval Model Based On Temporal-Spatial Correlation

There is an austere challenge to information retrieval and management following with the development of information technology and the explosive increase of multimedia information resources.People's understanding of the media content and semantic is often more concerned about than the type of media in multimedia retrieval. Therefore does not meet the needs of monomodal results for multimedia information retrieval.It is very important to improve a novel retrieval model for query multimodal media to realization of cross-media retrieval(CMR).Aiming at the problem of the "semantic gap" and the "dimensionality curse",this paper discussed the theoretical model and application development of the cross-media retrieval.A cross-media retrieval model based on temporal-spatial correlation was put forward;Methods and algorithm were given according to the feature of cross-media retrieval.The contents are as followed:1.A semantic machine was design from Turing machine imitation,and the information processing programs were given.To overcome the complex problem of semantic mapping,a semantic hierarchical structure should adopt to reduce the "semantic gap".A nonlinear hybrid classifier based on support vector hidden Markov models(SVHMM) was design for implementation semantic mapping and learing,and used ensemble learning of selectivity of multi-classifier(SH-EL) to improve the precision of the classification.2.The methods of feature extraction and fusion of multimedia were given for processing high-dimensional data.In order to reduce effect the "dimensionality curse",a linear dimensionality reduction algorithm based on principal independent component analysis(PICA) was used to decreasing redundancy of features and realization multimodal features fusion.3.According to Shannon information theory,calculation methods of similarity and correlation were given.In view of temporal-spatial correlation of media,the cloud model and fuzzy C-means based on temporal-spatial clustering(CFCMTSC) algorithm was used to implementation space-time correlation clustering analysis.Similarity and correlation matching of system were based on results of clustering.This will reduce the range of information retrieval and enhance efficiency of information retrieval.The precision of retrieval was improved by the relevance feedback technology,and availability of search engine was improved by search results clustering.4.Related algorithms and theoretical analysis were discussed,and experiments analysis and verification were carried out in datasets with temporal and spatial attribute. The results show that this model can be applied to temporal-spatial cross-media retrieval, and with high retrieval efficiency and ranking.Finally,cross-media retrieval system architecture was designed background of a temporal spatial multimedia database (TSMD) project,and implementation and application effect were given.